{"id":"W4361215960","doi":"10.1371/journal.pdig.0000208","title":"Developing better digital health measures of Parkinson’s disease using free living data and a crowdsourced data analysis challenge","year":2023,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute; National Institutes of Health; National Institute of Neurological Disorders and Stroke; Sanofi; Merck KGaA; Icahn School of Medicine at Mount Sinai; Eli Lilly and Company; National Center for Advancing Translational Sciences; Michael J. Fox Foundation for Parkinson's Research","keywords":"Benchmarking; Leverage (statistics); Data collection; Dyskinesia; Medicine; Disease; Citizen science; Digital health; Real world data; Parkinson's disease; Confounding; Data science; Polychoric correlation; Psychology; Computer science; Machine learning; Health care; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004575682,0.0003320675,0.0009083314,0.0005079468,0.0002462485,0.0002017646,0.0006079215,0.00005609995,0.00001004936],"category_scores_gemma":[0.0006190675,0.000300212,0.0001038886,0.0009417645,0.00008227777,0.001019708,0.001623839,0.0001449113,0.00001300099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000240529,"about_ca_system_score_gemma":0.001185605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000196669,"about_ca_topic_score_gemma":0.000119091,"domain_scores_codex":[0.996639,0.00007228641,0.0006830266,0.001103795,0.0007874717,0.0007144944],"domain_scores_gemma":[0.9959616,0.000189863,0.0003544442,0.002418127,0.00007962101,0.0009963155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000540306,0.002260907,0.8083354,0.004539456,0.005651618,0.0003679155,0.001716806,0.00002156241,0.00001212253,0.0001395325,0.001642199,0.1747721],"study_design_scores_gemma":[0.003070011,0.0006010918,0.9059474,0.003510741,0.001721363,0.00002726164,0.001138029,0.06931871,0.0000104366,0.001144564,0.0126759,0.0008344925],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9693039,0.00453173,0.003454206,0.005919411,0.00006735728,0.0008571327,0.01548814,0.0002623305,0.0001157789],"genre_scores_gemma":[0.9916689,0.0008195033,0.001552261,0.0006399378,0.0001122484,0.00001197071,0.005098931,0.00006251445,0.00003368349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1739376,"threshold_uncertainty_score":0.999945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.204904671327659,"score_gpt":0.3579831517499391,"score_spread":0.1530784804222801,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}